This work investigates the performance of an integrated sensing and communication (ISAC) system exploiting inverse virtual aperture (IVA) for imaging moving extended targets in vehicular scenarios. A base station (BS) operates as a monostatic sensor using MIMO-OFDM waveforms. Echoes reflected by the target are processed through motion-compensation techniques to form an IVA range-Doppler (cross-range) image. A case study considers a 5G NR waveform in the upper mid-band, with the target model defined in 3GPP Release 19, representing a vehicle as a set of spatially distributed scatterers. Performance is evaluated in terms of image contrast (IC) and the root mean squared error (RMSE) of the estimated target-centroid range. Finally, the trade-off between sensing accuracy and communication efficiency is examined by varying the subcarrier allocation for IVA imaging. The results provide insights for designing effective sensing strategies in next-generation radio networks.
@article{arxiv.2601.16664,
title = {OFDM-Based ISAC Imaging of Extended Targets via Inverse Virtual Aperture Processing},
author = {Michael Negosanti and Lorenzo Pucci and Andrea Giorgetti},
journal= {arXiv preprint arXiv:2601.16664},
year = {2026}
}
Comments
6 pages; This paper was presented at the IEEE JC&S Symposium 2026